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Maximizing Multi-scale Spatial Statistical Discrepancy

Weishan Dong, Renjie Yao, Chunyang Ma, Changsheng Li, Lei Shi, Lu Wang, Yu Wang, Peng Gao, Junchi Yan
2014 Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14  
A grid hierarchy encoding multi-scale information is employed, making the algorithm capable of maximizing spatial discrepancies with multi-scale structures and irregular shapes.  ...  multi-scale discrepancy boundaries.  ...  We refer to it as the multi-scale spatial discrepancy, which is ubiquitous in spatial data.  ... 
doi:10.1145/2661829.2662007 dblp:conf/cikm/DongYMLSWWGY14 fatcat:2iurv4b4c5hyfa3ew6mn4m2a6m

Multivariate regression methods for estimating velocity of ictal discharges from human microelectrode recordings

Jyun-you Liou, Elliot H Smith, Lisa M Bateman, Guy M McKhann, Robert R Goodman, Bradley Greger, Tyler S Davis, Spencer S Kellis, Paul A House, Catherine A Schevon
2017 Journal of Neural Engineering  
Moreover, the negative peak and maximal descent methods proved to be more robust against reduced spatial sampling challenges.  ...  Main results-Over 90% of discharges met statistical criteria for propagation across the sampled cortical territory.  ...  The median direction also shifted as seen previously (negative peak: 89 degrees, median multi-sample test, p < 0.001; maximal descent: 94 degrees, median multi-sample test, p < 0.001, N = 111).  ... 
doi:10.1088/1741-2552/aa68a6 pmid:28332484 pmcid:PMC5728389 fatcat:ngick4pb3fa7dmlkalu7dbc3xi

A Discrepancy-based Framework to Compare Robustness between Multi-Attribute Evaluations [article]

Juste Raimbault
2016 arXiv   pre-print
Statistical robustness computation methods are highly dependent of underlying statistical models.  ...  Multi-objective evaluation is a necessary aspect when managing complex systems, as the intrinsic complexity of a system is generally closely linked to the potential number of optimization objectives.  ...  Acknowledgments The author would like to thank Julien Keutchayan (Ecole Polytechnique de Montréal) for suggesting the original idea of using discrepancy, and anonymous reviewers for the useful comments  ... 
arXiv:1608.00840v1 fatcat:mmqbcydprzh3rptbwwefdeq7ay

A Discrepancy-Based Framework to Compare Robustness Between Multi-attribute Evaluations [chapter]

Juste Raimbault
2016 Complex Systems Design & Management  
Data are associated with geographical extent of statistical units, allowing computation of spatial analysis indicators. Fig. 1 . 1 Maps of Metropolitan Segregation.  ...  In that space, data will represent more or less well real systems, depending e.g. on initial scale, precision of data, missing data.  ... 
doi:10.1007/978-3-319-49103-5_11 dblp:conf/csdm/Raimbault16 fatcat:bgwk2adtirgajnjudmw3o2w64i

The Relevance of Local-Scale Relationships to Habitat Management and Landscape Patterns

Joseph J. Nocera, Graham J. Forbes, G. Randy Milton
2008 Avian Conservation and Ecology  
A balanced view of scale in spatial statistical analysis. Ecography 25:626-640. Erickson, J. L., and S. D. West. 2003.  ...  A multi-scaled analysis of avian response to habitat amount and fragmentation in the Canadian dry mixed-grass prairie. Landscape Ecology 21:1045-1059.  ...  Ideally, we would seek to have the latter two items equal the first, or we can sample at multiple scales in attempt to capture the discrepancy.  ... 
doi:10.5751/ace-00222-030104 fatcat:qwn4quycdrd4jlhyllwilhg36q

A new method (M3Fusion-v1) for combining observations and multiple model output for an improved estimate of the global surface ozone distribution

Kai-Lan Chang, Owen R. Cooper, J. Jason West, Marc L. Serre, Martin G. Schultz, Meiyun Lin, Virginie Marécal, Béatrice Josse, Makoto Deushi, Kengo Sudo, Junhua Liu, Christoph A. Keller
2018 Geoscientific Model Development Discussions  
We show that our fused product has an improved mean squared error compared to the simple multi-model ensemble mean.</p>  ...  </strong> We have developed a new statistical approach (M3Fusion) for combining surface ozone observations from thousands of monitoring sites around the world with the output from multiple atmospheric  ...  Correcting multi-model bias: A common practice of studying the model discrepancy in the spatial fields is to fit a statistical model for their differences from observations on the whole spatial domain,  ... 
doi:10.5194/gmd-2018-183 fatcat:pg6y7qwtmrh6vpun7rcyap7gne

Variability of fMRI-response patterns at different spatial observation scales

Tonio Ball, Thomas P.K. Breckel, Isabella Mutschler, Ad Aertsen, Andreas Schulze-Bonhage, Jürgen Hennig, Oliver Speck
2011 Human Brain Mapping  
For this purpose, responses are statistically detected over a range of spatial scales using a family of Gaussian filters, with small filters being related to fine and large filters-to coarse spatial scales  ...  Our findings illustrate the potential of multi-scale fMRI analysis to reveal novel features in the spatial organization of human brain responses. Hum Brain Mapp 33:1155-1171,  ...  First, in scale search for brain responses using multi-filter analysis, multiple statistical tests are performed for different spatial scales, these multiple tests need to be taken into account to control  ... 
doi:10.1002/hbm.21274 pmid:21404370 fatcat:rn4jwrnf3ngqbdyviydq7qb2ju

Bone tumor segmentation from MR perfusion images with neural networks using multi-scale pharmacokinetic features

A.F. Frangi, M. Egmont-Petersen, W.J. Niessen, J.H.C. Reiber, M.A. Viergever
2001 Image and Vision Computing  
Multi-scale blurred versions of the parametric images together with a multi-scale formulation of the local image entropy turned out to be the most relevant features in distinguishing the tissues of interest  ...  A multi-scale analysis of the parametric perfusion images is applied to incorporate contextual information.  ...  Given the (multi-scale) local support of these features, they encode spatial information not present in the original parametric images (which themselves contain solely temporal information).  ... 
doi:10.1016/s0262-8856(01)00044-0 fatcat:7fapu2w6b5ctzasr2cdkniyknq

Multidimensional Texture Analysis for Improved Prediction of Ultrasound Liver Tumor Response to Chemotherapy Treatment [chapter]

Omar S. Al-Kadi, Dimitri Van De Ville, Adrien Depeursinge
2016 Lecture Notes in Computer Science  
The characterization of the speckle patterns is performed via state-of-the-art multi-orientation and multi-scale circular harmonic wavelet (CHW) frames analysis of the envelope of the radio-frequency signal  ...  To maximize the difference between the two conditions, the fractal signatures are derived from multi-scale circular frequency analysis of the acoustic properties of the envelope RF signal for assessing  ...  Circular Harmonic Wavelets A natural way of assessing the echo signal f (x, y) is to analyse its statistical properties at different spatial scales.  ... 
doi:10.1007/978-3-319-46720-7_72 fatcat:oevblhrerfdqrkphdhecbn442i

HGMR: Hierarchical Gaussian Mixtures for Adaptive 3D Registration [chapter]

Benjamin Eckart, Kihwan Kim, Jan Kautz
2018 Lecture Notes in Computer Science  
Our method, Hierarchical Gaussian Mixture Registration (HGMR), constructs a top-down multi-scale representation of point cloud data by recursively running many small-scale data likelihood segmentations  ...  We leverage the resulting representation using a novel optimization criterion that adaptively finds the best scale to perform data association between spatial subsets of point cloud data.  ...  Multiscale Adaptivity Real-world point clouds often exhibit large spatial discrepancies in sampling sparsity and geometric complexity, and so different parts of the scene may benefit from being represented  ... 
doi:10.1007/978-3-030-01267-0_43 fatcat:k62u7b225jandnfffejxkjjg6i

Bayesian Approach to Foreground Removal [article]

J. Jewell , S. Levin
1999 arXiv   pre-print
We propose a strategy for the regularization of solutions allowing a spatially varying spectral index, and discuss possible computational approaches such as multi-scale stochastic relaxation.  ...  Our ability to extract the maximal amount of information from future observations at gigahertz frequencies depends on our ability to separate the underlying cosmic microwave background (CMB) from galactic  ...  Generalization of Multi-Resolution Bayesian Inference Returning to the multi-resolution setting described before, we want to discuss Bayesian methods proceeding from coarse to fine scales.  ... 
arXiv:astro-ph/9903201v2 fatcat:47y4fqvo5jhztp7x5ktsb4k6rm

Discriminatively weighted multi-scale Local Binary Patterns: Applications in prostate cancer diagnosis on T2W MRI

Haibo Wang, Satish Viswanath, Anant Madabuhshi
2013 2013 IEEE 10th International Symposium on Biomedical Imaging  
Inspired by supervised learning, our methodology aims to learn the multi-scale, weight vector by minimizing the Hamming scores between positive class samples and jointly maximizing the scores between positive  ...  In this paper, we present discriminatively weighted Local Binary Patterns (DWLBP), a new similarity metric to match Multi-scale LBP (MsLBP) in Hamming space.  ...  It is necessary to define a weight vector so as to account for the statistical significance of information at the salient scale by measuring the dissimilarity between a pair of multi-scale LBPs.  ... 
doi:10.1109/isbi.2013.6556496 dblp:conf/isbi/WangVM13 fatcat:o4udsimejvfy7j5meymbkb7e6a

Multi-resolution analysis for region of interest extraction in thermographic nondestructive evaluation

B. Ortiz-Jaramillo, H. A. Fandiño Toro, H. D. Benitez-Restrepo, S. A. Orjuela-Vargas, G. Castellanos-Domínguez, W. Philips, Karen O. Egiazarian, Sos S. Agaian, Atanas P. Gotchev, John Recker, Guijin Wang
2012 Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II  
Also, the Gaussian scale is used to analyze details in the image using multi-resolution analysis avoiding low contrast, non-uniform heating and selection of the Gaussian window size.  ...  The non-uniform heating affects low spatial/frequencies and hinders the detection of relevant points in the image.  ...  We believe that the combination of multi-resolution analysis, local correlation and statistical tests is useful in other contexts.  ... 
doi:10.1117/12.912079 dblp:conf/ipas/JaramilloTBVCP12 fatcat:nkragiestraljpvscncr6siiwm

Wavelet-based multi-resolution statistics for optical imaging signals: Application to automated detection of odour activated glomeruli in the mouse olfactory bulb

Brice Bathellier, Dimitri Van De Ville, Thierry Blu, Michael Unser, Alan Carleton
2007 NeuroImage  
Discrepancies in the ellipsoid shapes are due to discrepancies in the localisation of the extremum. Scale bar: 200 μm.  ...  Multi-resolution analysis of the statistical maps The framework also allowed us to explore the multi-resolution structure of the activation patterns.  ... 
doi:10.1016/j.neuroimage.2006.10.038 pmid:17185002 fatcat:tmhy4iyvvvbxdnh7xt3dzjktnu

Nonhierarchical multi-model fusion using spatial random processes

Shishi Chen, Zhen Jiang, Shuxing Yang, Daniel W. Apley, Wei Chen
2015 International Journal for Numerical Methods in Engineering  
The other two approaches model the true response as the sum of one simulation model and a corresponding discrepancy function, and differ in their assumptions regarding the statistical behavior of the discrepancy  ...  functions, such as independence with the true response or a common spatial correlation function.  ...  In the third approach, a fully correlated multi-response SRP structure with a common spatial correlation function is adopted for the simulation models and their discrepancy functions altogether.  ... 
doi:10.1002/nme.5123 fatcat:ygmegy4mfjh4fes7o54mjqafim
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